Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security

数据优化应急筛选：一种电力系统安全性的机器学习方法

Abstract: Ensuring the security of the power system is essential for stability and reliability, especially in the event of disruption. Effective classification of contingency in power systems enables proactive decision-making and mitigates large-scale breakdowns and failures.

摘要： 确保电力系统的安全性对于维持其稳定性和可靠性至关重要，尤其是在发生中断的情况下。对电力系统中的应急情况进行有效分类，能够实现主动决策，并减轻大规模故障和崩溃的影响。

This study explores the use of machine learning algorithms to classify security levels of contingencies in power systems into safe, moderate or severe classes. For this approach, Newton-Raphson load flow method extracts system data from contingency scenarios, using Overall Performance Index (OPI) as safety measure.

本研究探讨了利用机器学习算法将电力系统应急情况的安全等级分类为“安全”、“中度”或“严重”三个类别。在该方法中，研究采用牛顿-拉夫逊（Newton-Raphson）潮流计算法从应急场景中提取系统数据，并使用综合性能指标（OPI）作为安全衡量标准。

For data pre-processing, Synthetic Minority Over-Sampling Technique (SMOTE) and Principal Component Analysis (PCA) is used to address class imbalance and reduce dimensionality, respectively. K-Nearest Neighbours (KNN), Random Forest (RF) and Support Vector Machines (SVM) is trained and evaluated on datasets generated through N-k contingency scenarios for k equal 1, 2, and 3 on IEEE-14 and IEEE-30 bus systems using four hybrid pre-processing configurations: normalized, SMOTE-balanced, PCA-transformed, and a combined SMOTE PCA-transformed.

在数据预处理方面，研究分别使用了合成少数类过采样技术（SMOTE）和主成分分析（PCA）来解决类别不平衡问题并降低数据维度。研究在 IEEE-14 和 IEEE-30 总线系统上，针对 k=1、2 和 3 的 N-k 应急场景生成数据集，并使用四种混合预处理配置（归一化、SMOTE 平衡、PCA 转换以及 SMOTE 与 PCA 组合转换）对 K-近邻（KNN）、随机森林（RF）和支持向量机（SVM）模型进行了训练与评估。

Performance is assessed by precision, recall and F1 score, with priority given to the severe contingency classes. The RF achieved the highest F1 scores of 0.97 in IEEE-30 and 0.86 in IEEE-14, SVM benefits significantly from PCA and improves the accuracy of the classification, while KNN is best suited for SMOTE and PCA conversion.

性能评估指标包括精确率、召回率和 F1 分数，并优先考虑“严重”应急类别。结果显示，随机森林（RF）在 IEEE-30 和 IEEE-14 系统中分别达到了 0.97 和 0.86 的最高 F1 分数；支持向量机（SVM）从 PCA 中获益显著，分类准确率得到提升；而 K-近邻（KNN）则最适合 SMOTE 和 PCA 的组合转换。

The findings show that PCA contributes more than SMOTE to the overall performance of the model. However, SMOTE improves recall but can introduce false positives and is therefore a compromise of accuracy. This study highlights machine learning as a scalable and powerful alternative to traditional contingency analysis, which improves the assessment of security in real time.

研究结果表明，PCA 对模型整体性能的贡献大于 SMOTE。然而，SMOTE 虽然提高了召回率，但也可能引入误报，因此在准确性上是一种折中。本研究强调了机器学习作为传统应急分析的一种可扩展且强大的替代方案，能够有效提升实时安全评估的能力。